Metric for evaluating indoor positioning systems

ABSTRACT

A method, apparatus, computer program or system includes obtaining fingerprints including a piece of position information and gathered in a venue; determining a first metric based on the fingerprints indicative of a quality value of the obtained fingerprints that indicates for each piece of position information of the fingerprints whether or not the quality and/or a quantity of the obtained fingerprints with respect to the piece of position information is sufficient; determining a second metric based on the obtained fingerprints indicative of a quality value of an infrastructure of the venue, the fingerprints are gathered from one or more radio nodes of the infrastructure, and the second metric indicates for each of the pieces of position information of each of the respective fingerprints whether or not the quality of the infrastructure is sufficient; determining a third metric indicative of an evaluation of the quality of the infrastructure of the venue based on the second metric; and outputting the first metric, the second metric and/or the third metric.

CROSS REFERENCE TO PRIOR APPLICATION

This application is a continuation under 35 U.S.C. § 120 and 37 C.F.R. §1.53(b) of U.S. patent application Ser. No. 15/631,662 filed Jun. 23,2017, which is hereby incorporated by reference in its entirety.

FIELD

The following disclosure relates to the field of indoor positioningsystems, in particular of an evaluation metric which might be used forachieving accurate indoor positioning performance.

BACKGROUND

Indoor positioning requires novel systems and solutions that arespecifically developed and deployed for this purpose. The “traditional”positioning technologies, which are mainly used outdoors, for instancesatellite and cellular positioning technologies, cannot deliver suchperformance indoors that would enable seamless and equal navigationexperience in both environments.

The required positioning accuracy (within 2 to 3 meters), coverage(˜100%) and floor detection are challenging to achieve with satisfactoryperformance levels with the systems and signals that were not designedand specified for the indoor use cases in the first place.Satellite-based radio navigation signals simply do not penetrate throughthe walls and roofs for the adequate signal reception and the cellularsignals have too narrow bandwidth for accurate ranging by default.

Several indoor-dedicated solutions have already been developed andcommercially deployed during the past years, for instance solutionsbased on pseudolites (GPS-like short-range beacons), ultra-soundpositioning, BTLE signals (e.g. High-Accuracy Indoor Positioning, HAIP)and Wi-Fi fingerprinting. What is typical to these solutions is thatthey require either deployment of totally new infrastructure (beacons,tags to name but a few non-limiting examples) or manual exhaustive radiosurveying of the buildings including all the floors, spaces and rooms.This is rather expensive and will take a considerable amount of time tobuild the coverage to the commercially expected level, which in somecases narrowed the potential market segment only to very thin customerbase, for instance for health care or dedicated enterprise solutions.Also, the diversity of these technologies makes it difficult to build aglobally scalable indoor positioning solution, and the integration andtesting will become complex if a large number of technologies needs tobe supported in the consumer devices, such as smartphones.

For an indoor positioning solution to be commercially successful, thatis, i) being globally scalable, ii) having low maintenance anddeployment costs, and iii) offering acceptable end-user experience, thesolution needs to be based on an existing infrastructure in thebuildings and on existing capabilities in the consumer devices. Thisleads to an evident conclusion that the indoor positioning needs to bebased on Wi-Fi- and/or Bluetooth (BT)-technologies that are alreadysupported in every smartphone, tablet, laptop and even in the majorityof feature phones. It is, thus, required to find a solution that usesthe Wi-Fi- and BT-radio signals in such a way that makes it possible toachieve 2 to 3 meter horizontal positioning accuracy, close to 100%floor detection with the ability to quickly build the global coveragefor this approach.

Further, a novel approach for radio-based indoor positioning that modelsfor instance the Wi-Fi-radio environment (or any similar radio e.g.Bluetooth) from observed Received Signal Strength (RSS)-measurements astwo-dimensional radiomaps and is hereby able to capture the dynamics ofthe indoor radio propagation environment in a compressable and highlyaccurate way. This makes it possible to achieve unprecedented horizontalpositioning accuracy with the Wi-Fi signals only within the coverage ofthe created radiomaps and also gives highly reliable floor detection.

To setup indoor positing in a building, the radio environment in thebuilding needs to be surveyed. This phase is called “radiomapping”. Inthe radiomapping phase samples containing geolocation (like latitude,longitude, altitude; or x, y, floor) and radio measurements (Wi-Fiand/or Bluetooth radio node identities and signal strengths). Havingthese samples allows understanding how the radio signals behave in thebuilding. This understanding is called a “radiomap”. The radiomapenables localization capability to devices when they observe varyingradio signals, the signals can be compared to the radiomap resulting inthe location information.

The radio samples for the radiomap may be collected with separate toolsor crowd-sourced from the user devices. While automated crowd-sourcingcan enable indoor localization in large amount of buildings, manual datacollection using special tools may be the best option, when the highestaccuracy is desired.

SUMMARY

When manually collecting radio data in a venue (e.g. a building,shopping mall, university to name but a few non-limiting examples), theuser (e.g. a person performing the radio data collection) will face theobvious question, if and/or when the user has collected enough data(e.g. all over the venue at sufficient density) so that accurate indoorpositioning and/or floor detection can be performed. And even if thesample coverage and density are high enough, it may still be that theoverall positioning quality is not high, because the building does nothave enough radio nodes (e.g. Wi-Fi and/or Bluetooth nodes) to supportindoor positioning. It would be appreciated to present this complexinformation to a non-professional user so that sufficient data can becollected more easily.

Moreover, it is important to indicate insufficient radio infrastructurein time before the user collects data thoroughly in the venue and thusconsumes a lot of time for the radio survey. In any case, the radiosurvey must only be done thoroughly after the radio infrastructuresufficiency has been ensured.

It is thus, inter alia, an object of the following embodiments toprovide an evaluation of the sufficiency of data collection (offingerprints) and/or of the sufficiency of the radio infrastructure forindoor positioning systems.

According to a first exemplary embodiment of the present followingembodiments, a method is disclosed, the method comprising:

-   -   obtaining a plurality of fingerprints, wherein each fingerprint        comprises a piece of position information, and wherein each        fingerprint is gathered in a venue;    -   determining a first metric based at least partially on the        obtained plurality of fingerprints, wherein the first metric is        indicative of a quality value of the obtained plurality of        fingerprints, wherein the first metric indicates for each piece        of position information of the plurality of fingerprints whether        or not the quality and/or a quantity of the obtained        fingerprints with respect to the piece of position information        is sufficient;    -   determining a second metric based at least partially on the        obtained plurality of fingerprints, wherein the second metric is        indicative of a quality value of an infrastructure of the venue,        wherein the plurality of fingerprints are gathered from one or        more radio nodes comprised by the infrastructure, and wherein        the second metric indicates for each of the pieces of position        information of each of the respective fingerprints whether or        not the quality of the infrastructure is sufficient;    -   determining a third metric indicative of an evaluation of the        quality of the infrastructure of the venue based at least        partially on the second metric; and    -   outputting the first metric, the second metric and/or the third        metric.

This method may for instance be performed and/or controlled by anapparatus, for instance a server. Alternatively, this method may beperformed and/or controlled by more than one apparatus, for instance aserver cloud comprising at least two servers. Alternatively, the methodmay for instance be performed and/or controlled by an electronic device,e.g. a mobile terminal. For instance, the method may be performed and/orcontrolled by using at least one processor of the server or theelectronic device.

According to a further exemplary aspect of the following embodiments, acomputer program is disclosed, the computer program when executed by aprocessor causing an apparatus, for instance the server, to performand/or control the actions of the method according to the firstexemplary embodiment.

The computer program may be stored on computer-readable storage medium,in particular a tangible and/or non-transitory medium. The computerreadable storage medium could for example be a disk or a memory or thelike. The computer program could be stored in the computer readablestorage medium in the form of instructions encoding thecomputer-readable storage medium. The computer readable storage mediummay be intended for taking part in the operation of a device, like aninternal or external memory, for instance a Read-Only Memory (ROM) orhard disk of a computer, or be intended for distribution of the program,like an optical disc.

According to a further exemplary aspect of the following embodiments, anapparatus is disclosed, configured to perform and/or control orcomprising respective means for performing and/or controlling the methodaccording to the first exemplary embodiment.

The means of the apparatus can be implemented in hardware and/orsoftware. They may comprise for instance at least one processor forexecuting computer program code for performing the required functions,at least one memory storing the program code, or both. Alternatively,they could comprise for instance circuitry that is designed to implementthe required functions, for instance implemented in a chipset or a chip,like an integrated circuit. In general, the means may comprise forinstance one or more processing means or processors.

According to a further exemplary aspect of the following embodiments, anapparatus is disclosed, comprising at least one processor and at leastone memory including computer program code, the at least one memory andthe computer program code configured to, with the at least oneprocessor, cause an apparatus, for instance the apparatus, at least toperform and/or to control the method according to the first exemplaryembodiment.

The above-disclosed apparatus according to any aspect of the followingembodiments may be a module or a component for a device, for example achip. Alternatively, the disclosed apparatus according to any aspect ofthe following embodiments may be a device, for instance a server orserver cloud, or any other kind of electronic device, e.g. a mobile(e.g. smartphone, tablet, to name but a few non-limiting examples) or astationary device (e.g. navigation device comprised by a vehicle, toname but one non-limiting example). The disclosed apparatus according toany aspect of the following embodiments may comprise only the disclosedcomponents, for instance means, processor, memory, or may furthercomprise one or more additional components.

According to a further exemplary aspect of the following embodiments, asystem is disclosed, comprising:

an apparatus according to any aspect of the embodiments as disclosedabove, and an electronic device, wherein the electronic device isconfigured to gather one or more fingerprints.

The apparatus may for instance be a server or any other kind of mobileor stationary device, and is in the following also referred to as “firstapparatus”. The electronic device may for instance be a mobile (e.g.smartphone, tablet, to name but a few non-limiting examples) or astationary device (e.g. navigation device comprised by a vehicle, toname but one non-limiting example). The apparatus and the electronicdevice each may comprise a processor, and linked to the processor, amemory. The memory may for instance store computer program code forobtaining data associated with each road segment of at least one roadsegment, for obtaining probe data associated with the respective roadsegment, and for determining a sinuous driving metric. The processor isconfigured to execute computer program code stored in the memory inorder to cause the apparatus and/or the electronic device to perform oneor more desired actions. The memory may for instance be an exampleembodiment of a non-transitory computer readable storage medium, inwhich computer program code according to the embodiments may be stored.

In the following, exemplary features and exemplary embodiments of allaspects of the present embodiments will be described in further detail.

Each of the plurality of fingerprints may for instance stem from theelectronic device. Each of the plurality of fingerprints may forinstance be received from the electronic device, or from other entitythat transmits a fingerprint, e.g. to the first apparatus. One of theplurality of fingerprints may alternatively stem from an entity that isdifferent from the electronic device, e.g. a server of a computer. Theentity may for instance desire an evaluation of the plurality offingerprints of a venue, and to be provided the evaluation to theelectronic device, e.g. for enhancing the gathering of fingerprints ofthe venue for usage in indoor positioning and/or floor detection.

The determined third metric may for instance be used by a user. The usermay for instance be the individual performing a gathering of a pluralityof fingerprints for a venue (e.g. manual data collection), based onwhich gathered plurality of fingerprints indoor positioning and/or floordetection may be performed.

The electronic device may for instance be an electronic device. Theelectronic device may for instance be portable (e.g. weigh less than 5,4, 3, 2, or 1 kg). The electronic device may for instance comprise or beconnectable to a display for displaying a radiomap, and additionally avisualization of the determined third metric, e.g. that is guidingrespectively navigating a user, e.g. for gathering additional one ormore fingerprints of the venue, or identifying one or more parts (e.g.areas) of the venue in which additional infrastructure (e.g. radionodes) may for instance be added (e.g. installed). The electronic devicemay for instance comprise or be connectable to means for outputtingsound, e.g. in the form of spoken commands or information. Theelectronic device may for instance comprise or be connectable to one ormore sensors for determining the electronic devices position, such asradio-based indoor positioning from e.g. observed RSS-measurements ase.g. horizontal position, for in case it is available inside the venue,for instance a Global Navigation Satellite System (GNSS) receiver, e.g.in the form of a Global Positioning System (GPS) receiver.

A fingerprint comprises a piece of position information representing ahorizontal position (e.g. a location), and the fingerprint comprises oneor more identifiers of radio nodes, which transmitted signal isreceivable at the horizontal position represented by the positioninformation. Based on a respective identifier of a radio node, thehorizontal position of said radio node may for instance be determined.For instance, a database may comprise the horizontal position of a radionode corresponding to the identifier of the radio node. Additionally,the fingerprint may for instance comprise a received signal strength(RSS) of the one or more signals transmitted by the one or more radionodes. In case the fingerprint does not comprise the RSS, a value of theRSS may for instance be determined based, at least partially on thepiece of position information and the identifier of the respective radionode. Based on the identifier of the radio node, the horizontal positionmay for instance be determined. Using for instance a channel modelrepresenting the propagation of signals transmitted by the radio node,the RSS of signals received at the horizontal position of the RSS mayfor instance be determined. More accurate values for RSS are obtained incase the RSS is e.g. measured and being comprised by the fingerprint.

A fingerprint may for instance be gathered (e.g. measured) by theelectronic device. A fingerprint may for instance be gathered byobtaining a piece of position information (e.g. determining a horizontallocation) and by measuring signals transmitted by one or more radionodes, which are receivable at the horizontal location the electronicdevice is located during the gathering of the fingerprint.

The plurality of fingerprints are for instance gathered by a pluralityof electronic devices, wherein each of the plurality of electronicdevice e.g. measures signals transmitted from one or more radio nodes,which signal is receivable with one or more sensors (e.g. Bluetoothand/or Wi-Fi receiver(s)). Each of the plurality of fingerprintscomprises one or more identifiers of the one or more radio nodes, alsoreferred to as a “set of radio nodes”.

Each fingerprint may for instance comprise at least one value of areceived signal strength being associated with the piece of positioninformation comprised by the fingerprint and representing the locationof the venue from which the value of the signal strength is gathered(e.g. measured). Each fingerprint of the plurality of fingerprints mayfor instance represent a sample gathered (e.g. measured) by anelectronic device, wherein the RSS of all signals transmitted by one ormore radio nodes (also referred to as a set of radio nodes) receivableis measured. Each of such a fingerprint may for instance be measurede.g. by one or more sensors (e.g. receivers, BTLE and/or Wi-Fireceiver(s)) of the electronic device.

The infrastructure may for instance comprise one or radio nodes (e.g.Beacons or Wi-Fi Access Points) and their location in the venue. Thevenue may for instance be a building, shopping mall, office complex,public accessible location (e.g. station, airport or the like) to namebut a few non-limiting examples.

The first metric may for instance be represented by a first value. Thefirst value may for instance be indicative of e.g. a low, a high, oroptionally a medium state associated with the quality of the pluralityof fingerprints obtained for the infrastructure of the venue. The firstmetric may for instance be binary value, wherein the binary value mayfor instance be indicative of a low quality value, and a high qualityvalue. In case the first value is optionally indicative of a mediumstate, the second metric may for instance be represented by a valuebeing capable of being assigned with at least three different values.

The second metric may for instance be represented by a second value. Thesecond value may for instance be indicative of e.g. a low, a high, oroptionally a medium state associated with the quality of theinfrastructure of the venue. The second metric may for instance bebinary value, wherein the binary value may for instance be indicative ofa low quality value, and a high quality value. In case the second valueis optionally indicative of a medium state, the second metric may forinstance be represented by a value being capable of being assigned withat least three different values.

The third metric is determined based at least partially on the secondmetric. Alternatively, on the first metric and the second metric. Thesecond metric is more primary than the first metric, because theinfrastructure of the venue may be needed to be secured first, and thenthe fingerprints in the venue may be obtained. Since the second metricis more primary than the first metric, the second metric may forinstance be determined prior to determining the first metric. In casethe determined second metric is indicative of a low qualityinfrastructure, the first metric may not be determined, e.g. forenhancing the efficiency of the method by e.g. avoiding unnecessaryprocessing. In particular, this may apply since the method according tothe first aspect of the present embodiments may for instance requiresignificant processing power dependent upon the amount of fingerprintsused in the method according to the first aspect of the presentembodiments.

The third metric may for instance be indicative of one or more causesfor unsatisfactory indoor positioning accuracy and/or floor detection inthe venue. Further, the third metric may for instance be indicative ofcorrective actions to solve one or more causes for unsatisfactory indoorpositioning accuracy and/or floor detection.

The third metric is determined for instance be the determined secondmetric. For instance, the third metric is determined by analyzing thesecond metric, e.g. identifying one or more areas of the venue, in whichthe quality value of the infrastructure of the venue represents e.g. alow or a medium state. In case the second metric is represented by a“low” or “medium” value, the third metric is determined to represent thequality of the infrastructure is not sufficient. Additionally, the thirdmetric may for instance be determined to represent a suggestion to theuser that one or more radio nodes need to be added to the infrastructureof the venue, in particular in one or more areas of the venue having alow or medium quality in these areas of the venue of the infrastructure.In case the second metric is represented by a “high” value, the thirdmetric is determined to represent the quality of the infrastructure issufficient. Additionally, the third metric may for instance bedetermined to represent a suggestion to the user that no actions fromthe user are required.

Alternatively, the third metric may for instance be determined byevaluating the determined first metric and the determined second metric.In this case, the third metric may for instance be indicative of the(e.g. overall) quality of the obtained plurality of fingerprints and thequality of the infrastructure of the venue. Further, in this case, thethird metric may for instance be determined based at least partially onthe first metric and on the second metric. The underlying principle fordetermining the third (overall) evaluation of the quality of theobtained plurality of fingerprints and of the quality of theinfrastructure of the venue is as follows:

-   i) In case the first metric is represented by a “high” value and the    second metric is represented by a “high” value, the third metric is    determined to represent an (overall) high evaluation. Additionally,    the third metric may for instance be determined to represent a    suggestion to the user that no actions from the user are required.-   ii) In case the first metric is represented by a “low” value and the    second metric is represented by a “high” value, the third metric is    determined to represent an (overall) low evaluation. Additionally,    the third metric may for instance be determined to represent a    suggestion to the user that additional one or more fingerprints e.g.    in one or more areas of the venue having a low quality of the    obtained plurality of fingerprints need to be obtained.-   iii) In case the first metric is represented by a “low” value and    the second metric is represented by a “low” value, the third metric    is determined to represent an (overall) low evaluation.    Additionally, the third metric may for instance be determined to    represent a suggestion to the user that one or more radio nodes need    to be added to the infrastructure of the venue, in particular in one    or more areas of the venue having a low quality in these areas of    the venue of the infrastructure.-   iv) In case the first metric is represented by a “high” value and    the second metric is represented by a “low” value, the third metric    is determined to represent an (overall) low evaluation.    Additionally, the third metric may for instance be determined to    represent a suggestion to the user that one or more radio nodes need    to be added to the infrastructure of the venue, in particular in one    or more areas of the venue having a low quality in these areas of    the venue of the infrastructure.

The quality of the obtained fingerprints may for instance be indicativeof the amount of fingerprints which are obtained at one or more areas ofthe venue. The one or more areas are may for instance be defined by atleast two pieces of position information representing locations insidethe venue. The quality of the obtained fingerprints may for instance beindicative of the coverage of the one or more areas of the venue forwhich fingerprints are gathered and/or of the density of the gatheredfingerprints, which needs to be high enough to support accurate indoorpositioning and/or floor detection.

The quality of the infrastructure of the venue may for instance beindicative of the indoor positioning performance and/or floor detectionperformance being not high, since the venue does not have enough radionodes (e.g. beacons and/or Wi-Fi Access points) comprised by itsinfrastructure to support accurate indoor positioning and/or floordetection.

The positioning information may for instance be indicative of ahorizontal position and/or a floor of a venue. The positioninginformation may for instance comprise by at least one pair oflatitude/longitude coordinates, and additionally an altitude, or x,y-coordinates, and additionally a floor level of the venue.

The infrastructure may for instance comprise one or more radio nodes(e.g. beacons for indoor positioning and/or floor detection according tothe Bluetooth Low Energy specification, and/or Wi-Fi Access Points forindoor positioning and/or floor detection according to the WirelessLocal Area Network specification).

The radio nodes of the infrastructure of a venue may for instancetransmit one or more signals comprising at least an identifier of saidradio node. In case the one or more transmitted signals of the radionode is received, the radio node may for instance be identified based atleast partially on the identifier comprised by the one or more signals.

The determined third metric is then output, e.g. to the electronicdevice or to another apparatus that transfers the third metric to theelectronic device. The third metric may for instance be used forvisualizing the quality of fingerprints and/or the quality of theinfrastructure which is used for indoor positioning and/or floordetection in the venue.

Example embodiments thus make it possible to determine (e.g. at a serveror a server cloud) a third metric being indicative of an evaluation ofthe obtained plurality of fingerprints. The determined third metric mayfor instance be used in e.g. identifying a part (e.g. region and/orarea) of a venue where indoor positioning accuracy and/or floordetection may be unsatisfactory, and the third metric is indicative ofone or more causes for the unsatisfactory indoor positioning accuracyand/or floor detection. Further, the third metric may be used forderiving corrective actions to solve the one or more causes for theunsatisfactory indoor positioning accuracy and/or floor detection.

It should be noted that the step of determining the first metric and thestep of determining the second metric may take place in parallel. Forinstance, after obtaining the plurality of fingerprints, the first andthe second metric may be determined. The third metric may then bedetermined after determining the first and the second metric.Alternatively, the second metric may be determined prior to determiningthe first metric due to the second metric is more primary than the firstmetric—as already described above.

According to an exemplary embodiment of all aspects of the presentembodiments, the quality value of the obtained plurality of fingerprintsand/or the quality value of the infrastructure of the venue representone of the following states i) to iii):

i) infrastructure quality and/or fingerprint quality is low;ii) infrastructure quality and/or fingerprint quality is medium;iii) infrastructure quality and/or fingerprint quality is high.

As already disclosed above in this specification, the state i)represents that the quality of the infrastructure is low (e.g. notenough radio nodes are comprised by the infrastructure in one or moreaffected areas of the venue), or that the fingerprint quality is low(e.g. not enough fingerprints are gathered for one or more affectedareas of the venue), or the state iii) represents that theinfrastructure quality and the fingerprint quality is high, wherein inthe last case the plurality of fingerprints and the infrastructure usedfor performing indoor positioning and/or floor detection are sufficientto support accurate indoor positioning and/or floor detection.

The state ii) represents that the infrastructure quality and/or thefingerprint quality is medium. The additional state of “medium”representing the quality of the obtained fingerprints and/or of theinfrastructure of the venue may for instance be used to indicate one ormore areas of the venue in which additional one or more fingerprintsand/or additional one or more radio nodes being added to theinfrastructure may further enhance the performance of the indoorpositioning and/or floor detection.

In case the third metric is indicative of an evaluation of the qualityof the obtained plurality of fingerprints and the quality of theinfrastructure of the venue, the evaluation of the obtained plurality offingerprints may for instance represent one of the states i) to iii).

According to an exemplary embodiment, in case the second metric isindicative of the state i) represented by a quality value indicative ofthe infrastructure quality is low, the third metric is determined torepresent an overall low quality state (e.g. infrastructure qualityneeds to be enhanced since the quality of the infrastructure needs to beassured first in order to achieve more accurate indoor positioningand/or floor detection), independent of whether or not the first metricis indicative of that the quality of the obtained fingerprints issufficient.

In this exemplary embodiment, the second metric is more primary than thefirst metric, because the infrastructure may be needed to be securedfirst and only then a plurality of fingerprints should be obtained. Incase e.g. additional radio nodes are added to the infrastructure of thevenue, previously obtained fingerprints may for instance be obsolete,since the adding of additional one or more fingerprints changes thefingerprints which can be gathered in part of the venue where one ormore additional radio nodes are added.

According to an exemplary embodiment, in case the first metric indicatesthat the quality of the obtained plurality of fingerprints with respectto the position information is not sufficient, at least a part of theoutputted first metric represents that additional one or morefingerprints need to be obtained.

In this way, the user (e.g. the individual setting up the infrastructureand gathering radio data for providing indoor positioning service and/orfloor detection in a venue) is suggested, what kind of corrective theuser needs to take in order to provide seamless indoor positioningservice and/or floor detection).

According to an exemplary embodiment, in case the second metricindicates that the quality of the infrastructure is not sufficient, atleast a part of the outputted second metric and/or the outputted thirdmetric represents that the infrastructure needs to be expanded, andafter the expansion, additional one or more fingerprints need to beobtained.

The infrastructure of the venue may for instance be expanded by addingone or more radio nodes (e.g. beacons and/or Wi-Fi Access Points) to thevenue. In particular, at areas of the venue which are determinedaccording to the third metric of having a low quality of theinfrastructure, one or more of said radio nodes may for instance beadded. Further, the additional radio nodes may for instance be added atone or more areas of the venue, where no other radio nodes are located.

According to an exemplary embodiment, the first metric is determinedbased at least partially on a fingerprint density analysis, wherein thefingerprint density analysis comprises analyzing how many fingerprintsof the plurality of fingerprints are associated with an area of thevenue.

An area of the venue may for instance be a pre-defined or determinedaccording to pre-defined rules part (e.g. of a predefined size and shape(e.g. quadratic)) of the venue.

This may for instance be analyzed based at least partially on theplurality of fingerprints. For instance, after defining one or moreareas for the venue, for each of the area it may be checked how manyfingerprints of the obtained plurality of fingerprints are locatedwithin said area. This may for instance be determined based at leastpartially on the piece of position information of each respectivefingerprint. For each of the one or more areas of the venue, enoughfingerprints need to be obtained. For instance, whether or not enoughfingerprints are obtained for an area of the venue, may be determinedbased at least partially on a comparison with a reference valueindicating how many fingerprints should be associated or being obtainedfor each of the one or more areas of the venue. Based on the amount offingerprints associated with each of the one or more areas of the venue,the density of the fingerprints for each of said one or more areas mayfor instance be determined.

Further, the fingerprint density analysis may for instance comprise achecking of the determined density with a threshold value. In case apre-defined or defined according to pre-defined rules density of thefingerprints associated with one of the one or more areas of the venueis reached, saturation may occur. Thus, no further fingerprints for saidarea need to be obtained. Additional one or more fingerprints may thenfor instance not further enhance the performance of indoor positioningand/or floor detection performed within said area of the venue.

Analyzing how many fingerprints of the plurality of fingerprints areassociated with an area of the venue may for instance be performed byinterpolating distances between each respective fingerprints of the setof fingerprints, e.g. corresponding to the position information. Atleast the distances between adjacent fingerprints may for instance beinterpolated. For the interpolation, for instance so-called Delaunaytriangles may be formed based, at least partially, on the respectivefingerprints of the plurality of fingerprints, e.g. corresponding to therespective position information. The density of the fingerprints may forinstance represent whether or not enough fingerprints are obtained foran area of the venue, e.g. the sufficiency of the obtaining of thefingerprints may for instance be evaluated. The sufficiency may forinstance be estimated from the triangle edge lengths of the formedDelaunay triangles. In case the edge length is relatively short comparede.g. to a pre-defined or determined according to pre-defined rulesthreshold value (e.g. edge length of each respective triangle is belowthe threshold value), the fingerprint density analysis may for instancerepresent that enough fingerprints of an area of the venue are obtained.In case the edge length is relatively long compared e.g. to apre-defined or determined according to pre-defined rules threshold value(e.g. edge length of each respective triangle is above the thresholdvalue), the fingerprint density analysis may for instance represent thatnot enough fingerprints of an area of the venue are obtained. Incomparison to checking how many fingerprints of the obtained pluralityof fingerprints are located within an area of the venue, interpolatingdistances between each respective fingerprints of the plurality offingerprints may for instance resemble the density resulting of thechecking described above, but may give a result from a differentapproach.

According to an exemplary embodiment, the first metric is determinedbased at least partially on a similarity of nearby fingerprintsanalysis, wherein the similarity of nearby fingerprints analysiscomprises analyzing whether or not at least two fingerprints associatedwith an area of the venue (e.g. nearby (neighboring) fingerprintsassociated with adjacent horizontal positions in the venue; e.g.represented by the position information) comprise at least similaridentifiers of one or more radio nodes.

For instance, it may be analyzed if at least two of the plurality offingerprints associated with nearby locations (e.g. the pieces ofposition information comprised by the fingerprints indicate that thefingerprints were obtained at adjacent (e.g. neighboring horizontalpositions within the venue) comprise equal or comparable identifiers ofradio nodes (e.g. comprise an equal or similar or comparable set ofradio nodes). In case there is no similarity between the at least twofingerprints of the same area of the venue, for instance different setof radio nodes are comprised by the at least two fingerprints, it may beassumed that there is at least one obstacle of the venue (e.g. a wall,installation, object, to name but a few non-limiting examples) causingthe difference(s). For instance, in such an area of the venue, thefingerprints obtained of said area of the venue may not be denselyenough (e.g. more fingerprints need to be obtained from said area of thevenue compared to another area of the venue in which none of saidobstacles are located). In this way, for instance rapid signal strengthchange receivable, which is caused by said obstacles, may be captured sothat accurate indoor positioning and/or floor detection may beperformed.

According to an exemplary embodiment, the second metric is determinedbased at least partially on a number of radio nodes associated with theposition information comprised by each of the plurality of fingerprints.

For instance, based on the number of radio nodes, the more radio nodesare receivable at a location of the venue represented by the piece ofposition information comprised by the fingerprint, the better theinfrastructure at said location of the venue may be. Thus, it may beassumed that the quality of the infrastructure at said location isbetter. The number of radio nodes may for instance be determined basedat least partially on the number of identifiers comprised by eachrespective fingerprint. The more radio nodes may for instance be usedfor indoor positioning and/or floor detection at a given location of thevenue, the better the positioning respective floor detection accuracymay be.

According to an exemplary embodiment, the second metric is determinedbased at least partially on an average or a median value of one or morereceived signal strengths, wherein the average or the median value iscalculated based on each of the one or more received signal strengths ofthe one or more radio nodes of each respective fingerprint.

The average or median value of the observed RSS may for instance bedetermined by selecting the received signal strength value from each ofthe one or more radio nodes comprised by the respective fingerprint andcalculating the arithmetical average (e.g. mean value) or the medianaverage value.

Additionally, the analysis of the average or median value of theobserved RSS may for instance comprise comparing the determined averageor median value for each respective fingerprint of the plurality offingerprints to each other and/or checking the determined average ormedian value for each respective fingerprint of the plurality offingerprints against a threshold.

For instance, for ensuring that a certain average or median value isreached, the determined average or median value may for instance becompared with the threshold. In this way, a certain level of RSS may beensured. Having a pre-defined or determined according to pre-definedrules level of RSS in the venue enhances indoor positioning and/or floordetection. Further, the more variability there is, the more unique apattern of the RSS may be. Alternatively or additionally, the analysisof the average or median value of the observed RSS may for instancecomprise determining the variability of RSS for each of the plurality offingerprints.

Further, in case the value of the average or median signal strength islow, it may for instance be assumed that on average there cannot be muchvariability in the RSS.

In case a fingerprint does not comprise the RSS for one or more radionodes, the RSS may for instance be determined (e.g. calculated) based atleast partially on the horizontal position represented by the piece ofposition information comprised by the fingerprint, and the identifier ofthe one or more radio nodes, based on which the horizontal position ofthe one or more radio nodes can be determined, and further based on achannel propagation model the received signal strengths may bedetermined.

According to an exemplary embodiment, the second metric is determinedbased at least partially on a distribution analysis of one or morereceived signal strengths comprised by each fingerprint, wherein each ofthe one or more received signal strengths is compared to each other.

By comparing each of the one or more received signal strengths of one ormore radio nodes, wherein for each of the one or more radio nodes asignal is receivable at the horizontal position in the venue representedby the piece of position information of the respective fingerprint, thedistribution of the one or more radio nodes may for instance be assumed.For instance, it may not be sufficient to have one or two relativelyhigh values of RSS of one or two radio nodes, if the received signalstrengths of the other radio nodes is e.g. very low. In such a case, thevariability is low and indoor positioning and/or floor detectionperformance may be not optimal resulting in not very accurate indoorpositioning and/or floor detection. Thus, the quality of theinfrastructure may for instance be evaluated better in case thedistribution analysis shows that there are multiple radio nodes fromwhich the RSS is high.

According to an exemplary embodiment, the second metric is determined atleast partially based on a distribution analysis of gradients of one ormore received signal strengths, wherein the distribution analysis ofgradients comprises checking the one or more received signal strengthsof one or more radio nodes associated with one or more fingerprints ofadjacent locations represented by the piece of position information ofthe respective fingerprints for RSS variability.

The quality of the infrastructure (e.g. of an area of the venue) may forinstance be evaluated better (e.g. high) in case the higher the receivedsignal strength variability is from horizontal position to horizontalposition (also referred to as from point-to-point). In such a case, theadjacent horizontal positions may for instance have more unique RSSpatterns. Then, the horizontal positions may for instance bedistinguished from each other more reliably. For instance, noisemeasurements may be used to distinguish the horizontal locations fromeach other. The more easily horizontal positions may be distinguishedfrom each other, the better the performance of indoor positioning and/orfloor detection is. Thus, the quality of the infrastructure may beevaluated better (e.g. with “high”) in case the variability of receivedsignal strengths is high from horizontal position to horizontal positionof the venue.

According to an exemplary embodiment, the third metric is outputted forusage as a visualization in a radiomap, wherein the visualizationrepresents the evaluation of the obtained plurality of fingerprints withrespect to each respective position information.

The radiomap may for instance represent a map of a venue, or at least apart (e.g. region or area) of the radiomap of the venue. These parts ofthe map of the venue may for instance be parts of a larger radiomap.These parts may for instance pertain to different venue, or one or moredifferent floors of a venue. The map of the venue may have been divided,e.g. by a regular grid (the parts of the venue may then for instance be(e.g. quadratic) tiles). The radiomap as used herein refers to a map(e.g. of the venue) comprising fingerprints associated to a plurality ofhorizontal positions of the map. Based on a comparison of thefingerprints associated with the radiomap and an obtained fingerprint,indoor positioning and/or floor detection may be performed.

The radiomap may for instance be available to the electronic device bybeing stored in or at the electronic device, or by being accessible bythe electronic device, e.g. via a wireless or wire-bound connection e.g.to an apparatus that stores the radiomaps. This apparatus may be remotefrom the electronic device or may be included with the electronic deviceinto one device.

In an exemplary embodiment of a method according to the first aspect,the third metric may for instance be visualized and be overlaid with agraphical representation of map data. The visualization of the thirdmetric may for instance be indicative of the quality of fingerprintsand/or the quality of the infrastructure of the venue.

The visualization may for instance be used to suggest to the userwhether or not enough fingerprints for the venue are gathered in orderto perform accurate indoor positioning and/or floor detection. Further,the visualization may for instance be used to suggest to the userwhether or not the coverage of one or more areas of the venue for whichfingerprints are gathered and the density of the gathered fingerprintsis high enough. Further, the visualization may for instance be used tosuggest to the user—even if the coverage of areas of the venue for whichfingerprints are gathered and the density of the gathered fingerprintsis high enough—that the indoor positioning performance and/or floordetection performance may not be high, since the venue does not haveenough radio nodes (e.g. beacons and/or Wi-Fi Access points) comprisedby its infrastructure to support accurate indoor positioning and/orfloor detection.

According to an exemplary embodiment, the visualization comprises anindication to one or more areas of the venue where additional one ormore fingerprints need to be obtained and/or where the infrastructureneed to be expanded.

The visualization may for example guide a user to one or more areas ofthe venue, where in order to achieve accurate indoor positioning and/orfloor detection, the quality of the infrastructure and/or the quality ofan obtained plurality of fingerprints need to be enhanced. For instance,based on the third metric, one or more areas of the venue having e.g. aninfrastructure of low quality may be identified. In these one or moreareas additional radio nodes may for instance be added in order toenhance the quality of the infrastructure in the one or more areas. Forinstance, based on the third metric, one or more areas of the venue inwhich the quality of an obtained plurality of fingerprints is low, saidquality of the obtained plurality of fingerprints may for instance beenhanced by obtaining additional one or more fingerprints. Based on theobtained plurality of fingerprints, indoor positioning and/or floordetection can be performed.

According to an exemplary embodiment, the infrastructure is expandableby adding one or more radio nodes to the infrastructure of the venue.

According to an exemplary embodiment, the visualization is overlaid on agraphical representation of the radiomap.

The features and example embodiments of the invention described abovemay equally pertain to the different aspects according to the presentinvention.

It is to be understood that the presentation of the invention in thissection is merely by way of examples and non-limiting.

Other features of the invention will become apparent from the followingdetailed description considered in conjunction with the accompanyingdrawings. It is to be understood, however, that the drawings aredesigned solely for purposes of illustration and not as a definition ofthe limits of the invention, for which reference should be made to theappended claims. It should be further understood that the drawings arenot drawn to scale and that they are merely intended to conceptuallyillustrate the structures and procedures described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

In the figures show:

FIG. 1 a schematic block diagram of an example embodiment of a systemcomprising an example apparatus;

FIG. 2 a flow chart illustrating an example operation, e.g. in the atleast one apparatus of FIG. 4, of an example method;

FIG. 3 a schematic flow chart of an example embodiment of a method;

FIG. 4 a schematic block diagram of an example embodiment of anapparatus;

FIG. 5 depicts a sample visualization of a third metric determinedaccording to an exemplary embodiment of a method; and

FIG. 6 depicts a sample visualization of a third metric determinedaccording to an exemplary embodiment of a method.

DETAILED DESCRIPTION

The following description serves to deepen the understanding of thepresent invention and shall be understood to complement and be readtogether with the description as provided in the above summary sectionof this specification.

FIG. 1 is a schematic high-level block diagram of an example embodimentof a system.

System 100 comprises a server 110, which may alternatively embodied as aserver cloud (e.g. a plurality of servers connected e.g. via theInternet and providing services at least partially jointly), a database120, and an electronic device 130, of which three different realizationsare exemplarily shown: a mobile phone, a tablet, and a portablecomputer.

According to embodiments, electronic device 130 gathers a plurality offingerprints inside a venue from an infrastructure comprising one ormore radio nodes of the venue. These fingerprints are obtained by server110, and may for instance be stored in the database 120. Server 110determines a first metric and a second metric based at least partiallyon the obtained plurality of fingerprints. Based at least partially onthe determined first metric and the determined second metric, a thirdmetric is determined being indicative of an overall evaluation of thequality of the obtained fingerprints of the venue and of the quality ofthe infrastructure of the venue. The overall evaluation may for instancecomprise values being indicative of the quality of the obtainedfingerprints of the venue and of the quality of the infrastructure ofthe venue for one or more areas of the venue, in which one or more areasthe venue may be divided. By dividing the venue in one or more areas,and by determining an evaluation for each of the one or more areas ofthe venue, a user may for instance be provided with a suggestion whatactions might be necessary in order for enhancing or providing accurateindoor positioning and/or floor detection within the venue.

Communication between electronic device 130 and server 110 may forinstance take place at least partially in a wireless fashion, e.g. basedon cellular radio communication or on Wireless Local Area Network(WLAN), or on Bluetooth based communication, to name but a fewnon-limiting examples. In this way, mobility of electronic device 130 isguaranteed.

FIG. 2 shows a flow chart illustrating an example operation, e.g. in theat least one apparatus of FIG. 4, of an example method. Flow chart 200may for instance be performed by server 110 of FIG. 1.

In step 201, a plurality of fingerprints are obtained, e.g. by server110 of FIG. 1. The plurality of fingerprints are obtained e.g. byreceiving the plurality of fingerprints from an electronic device (e.g.electronic device 130 of FIG. 1) from another entity (not shown inFIG. 1) that transmits the plurality of fingerprints to the server, orthe plurality of fingerprints are obtained of a memory (e.g. database120 of FIG. 1), wherein the plurality of fingerprints are stored in thememory.

In step 202, a first metric is determined based at least partially onthe obtained plurality of fingerprints. The first metric is determined,e.g. by server 110 of FIG. 1. The determined first metric may then forinstance be stored in a memory, e.g. database 120 of FIG. 1.

In step 203, a second metric is determined based at least partially onthe obtained plurality of fingerprints. The second metric is determined,e.g. by server 110 of FIG. 1.

The determined second metric may then for instance be stored in amemory, e.g. database 120 of FIG. 1.

In step 204, a third metric is determined based at least partially onthe determined the determined second metric, in particular the thirdmetric is determined based at least partially on the determined firstmetric and the determined second metric. The third metric is determined,e.g. by server 110 of FIG. 1. The determined third metric may then forinstance be stored in a memory, e.g. database 120 of FIG. 1.

In step 205, the determined first metric, the second metric and/or thethird metric is output. At least one of the determined metrics isoutput, e.g. to electronic device 130 of FIG. 1. At least one of thedetermined metrics is output, e.g. by transmitting the at least onemetric to an electronic device (e.g. electronic device 130 of FIG. 1),e.g. from server 110 of FIG. 1, or from another entity (not shown inFIG. 1) that transmits the at least one metric to the electronic device.Optionally, prior to outputting at least one of the determined metricsto electronic device, the at least one metric is obtained of a memory(e.g. database 120 of FIG. 1), wherein the determined third metric isstored in the memory. In particular, the determined first metric and thedetermined third metric are output. For instance, the outputted firstmetric may for instance be for usage as a visualization in a radiomap,wherein the visualization represents the evaluation of the obtainedplurality of fingerprints with respect to the position information.Further, the outputted third metric may for instance be for usage asanother visualization in a radiomap, wherein, the visualizationrepresents the evaluation of the infrastructure of the venue withrespect to the position information. In particular, the outputted thirdmetric may for instance be used as a visualization on top thevisualization of the outputted first metric.

The exemplary flow chart 200 of FIG. 2 may for instance comprise one ormore of the further features described above, for instance performing afingerprint density analysis for determining the first metric. Further,the exemplary flow chart 200 of FIG. 2 may for instance comprise one ormore further features described in this specification, for instance acheck whether or not the quality of an infrastructure for which theplurality of fingerprints are obtained is high according to thedetermined second metric, and depending on the outcome of this check,determining the first metric subsequently.

Furthermore, steps 202 and 203 may be performed either serial (as shownin FIG. 2) or in parallel. Alternatively, step 203 may be performedprior to step 202.

FIG. 3 shows a schematic flow chart of an example embodiment of amethod.

In step 301, a plurality of fingerprints are obtained (see step 201 ofFIG. 1).

In step 302, a second metric is determined based at least partially onthe obtained plurality of fingerprints. The second metric is indicativeof a quality value of an infrastructure of a venue (e.g. a building, ora floor of a building like a shopping mall). Based on the obtainedplurality of fingerprints, which are gathered e.g. by an electronicdevice (e.g. electronic device 130 of FIG. 1) by a measurement ofsignals transmitted from one or more radio nodes comprised by theinfrastructure of the venue, the quality of said infrastructure may beevaluated and the second metric is determined accordingly.

In step 303, it is checked whether or not the quality of theinfrastructure is high. In case the quality of the infrastructure of thevenue is not sufficient (e.g. low, or not high), it may be suggested toa user—the user may for instance be the individual performing thegathering of a plurality of fingerprints for a venue, based on whichgathered plurality of fingerprints indoor positioning and/or floordetection may be performed—to add one or more radio nodes to theinfrastructure, as indicated by step 304. Thus, it may be suggested tothe user what kind of corrective actions the user needs to take. In thisway, a highly user friendly usability can be achieved.

In case, the quality of the infrastructure of the venue is sufficient(e.g. high), it may be proceeded with step 305. In step 305, a firstmetric is determined based at least partially on the obtained pluralityof fingerprints. The first metric is indicative of a quality value ofthe obtained plurality of fingerprints, e.g. whether or not enoughfingerprints are obtained for (an area of) the venue.

In step 306, it is checked whether or not the quality of the obtainedplurality of fingerprints is high. In case the quality of the obtainedplurality of fingerprints is not sufficient (e.g. low, or not high), forinstance because the density of fingerprints associated with an area ofthe venue is not high enough, it may be suggested to the user to obtainadditional one or more fingerprints (for the affected area of thevenue), as indicated by step 307. Further, as indicated by the arrowpointing back from step 307 to step 305, after obtaining additional oneor more fingerprints, the first metric may for instance be determinedagain in order to check whether or not now—after additional one or morefingerprints for (an area of) the venue are obtained—the quality of theobtained plurality of fingerprints is sufficient.

In case the quality of the obtained plurality of fingerprints issufficient (e.g. high), it is determined that for (an area of) the venuei) the quality of the infrastructure, and ii) the quality of theobtained plurality of fingerprints is sufficient so that accurate andhigh performance of indoor positioning and/or floor detection issupported.

In comparison to the flow chart 200 of FIG. 2, in flow chart 300 of FIG.3 the second metric is determined in step 302 prior to determining thefirst metric in step 305. This may be done, since the second metric ismore primary than the first metric. The infrastructure may be needed tobe secured first in (an area of) a venue and only then a plurality offingerprints in a high quality should be obtained. In order to checkwhether or not the quality of the infrastructure of a venue is high, atleast some fingerprints need to be obtained, indicated by step 301. Incase e.g. additional radio nodes are added to the infrastructure of thevenue, previously obtained fingerprints may for instance be obsolete,since the adding of additional one or more fingerprints changes thefingerprints which can be gathered in (one or more areas of) the venuein which one or more additional radio nodes are added. In this case, a“new” plurality of fingerprints may be obtained.

FIG. 4 is a schematic block diagram of an apparatus 400 according to anexemplary aspect, which may for instance represent the electronic device130 of FIG. 1. Alternatively, the schematic block diagram of theapparatus 300 according to an exemplary aspect may for instancerepresent server 110 of FIG. 1.

Apparatus 400 comprises a processor 410, working memory 420, programmemory 430, data memory 440, communication interface(s) 450, an optionaluser interface 460 and an optional sensor(s) 470.

Apparatus 400 may for instance be configured to perform and/or controlor comprise respective means (at least one of 410 to 470) for performingand/or controlling the method according to the first exemplary aspect.Apparatus 400 may as well constitute an apparatus comprising at leastone processor (410) and at least one memory (420) including computerprogram code, the at least one memory and the computer program codeconfigured to, with the at least one processor, cause an apparatus, e.g.apparatus 400 at least to perform and/or control the method according toexemplary aspects.

Processor 410 may for instance comprise a first metric determiner 411 asa functional and/or structural unit. First metric determiner 411 may forinstance be configured to determine a first metric (see step 202 of FIG.2). Processor 410 may for instance comprise a second metric determiner412 as a functional and/or structural unit. Second metric determiner 411may for instance be configured to determine a lane distance metric (seestep 203 of FIG. 2). Processor 410 may for instance comprise a thirdmetric determiner 413 as a functional and/or structural unit. Thirdmetric determiner 413 may for instance be configured to determine avehicle lane pattern metric (see step 204 of FIG. 2). Processor 410 mayfor instance further control the memories 420 to 440, the communicationinterface(s) 450, the optional user interface 460 and the optionalsensor(s) 470.

Processor 410 may for instance execute computer program code stored inprogram memory 430, which may for instance represent a computer readablestorage medium comprising program code that, when executed by processor410, causes the processor 410 to perform the method according to thefirst exemplary aspect.

Processor 410 (and also any other processor mentioned in thisspecification) may be a processor of any suitable type. Processor 410may comprise but is not limited to one or more microprocessor(s), one ormore processor(s) with accompanying one or more digital signalprocessor(s), one or more processor(s) without accompanying digitalsignal processor(s), one or more special-purpose computer chips, one ormore field-programmable gate array(s) (FPGA(s)), one or morecontroller(s), one or more application-specific integrated circuit(s)(ASIC(s)), or one or more computer(s). The relevant structure/hardwarehas been programmed in such a way to carry out the described function.Processor 410 may for instance be an application processor that runs anoperating system.

Program memory 430 may also be included into processor 410. This memorymay for instance be fixedly connected to processor 410, or be at leastpartially removable from processor 410, for instance in the form of amemory card or stick. Program memory 430 may for instance benon-volatile memory. It may for instance be a FLASH memory (or a partthereof), any of a ROM, PROM, EPROM and EEPROM memory (or a partthereof) or a hard disc (or a part thereof), to name but a few examples.Program memory 430 may also comprise an operating system for processor410. Program memory 430 may also comprise a firmware for apparatus 400.

Apparatus 400 comprises a working memory 420, for instance in the formof a volatile memory. It may for instance be a Random Access Memory(RAM) or Dynamic RAM (DRAM), to give but a few non-limiting examples. Itmay for instance be used by processor 410 when executing an operatingsystem and/or computer program.

Data memory 440 may for instance be a non-volatile memory. It may forinstance be a FLASH memory (or a part thereof), any of a ROM, PROM,EPROM and EEPROM memory (or a part thereof) or a hard disc (or a partthereof), to name but a few examples. Data memory 440 may for instancestore data, e.g. map data. Data may for instance represent at least onelink associated with a road segment of at least one road segment. Datamay comprise one or more sets of data, each set of data comprising oneor more representatives of at least one link associated with a roadsegment of at least one road segment.

Communication interface(s) 450 enable apparatus 400 to communicate withother entities, e.g. with server 110 of FIG. 1. The communicationinterface(s) 450 may for instance comprise a wireless interface, e.g. acellular radio communication interface and/or a WLAN interface) and/orwire-bound interface, e.g. an IP-based interface, for instance tocommunicate with entities via the Internet. Communication interface(s)may enable apparatus 400 to communicate with other entities, forinstance with server 110 of FIG. 1.

User interface 460 is optional and may comprise a display for displayinginformation to a user and/or an input device (e.g. a keyboard, keypad,touchpad, mouse, etc.) for receiving information from a user.

Sensor(s) 470 are optional and may for instance comprise a barometricsensor, e.g. to gather pressure information.

Some or all of the components of the apparatus 400 may for instance beconnected via a bus. Some or all of the components of the apparatus 400may for instance be combined into one or more modules.

FIG. 5 depicts a sample visualization of a third metric determinedaccording to an exemplary embodiment of a method.

In FIG. 5 an example of a visualization of a third metric showing aquality of fingerprints and a quality of infrastructure for a venue isshown. The venue is depicted by a map of the inside of the venue.Further, the map of the venue is overlaid with the visualization of thethird metric. For instance, the upper left area of the venue illustratedin FIG. 5 is overlaid by a hatched area with vertical lines indicatingthat the overall evaluation is high. Thus, in said area, enough radionodes are comprised by the infrastructure and enough fingerprints areobtained so that in said area accurate indoor positioning and/or floordetection is supported.

The area indicated by the visualization of an area filled with L-shapedpatterns of the third metric (illustrated in the middle of FIG. 5 andillustrated in the lower part of FIG. 5) relates to areas of the venue,in which the quality of the infrastructure is low. Thus, in said areas,not enough radio nodes are comprised by the infrastructure. In order tosupport accurate indoor positioning and/or floor detection, additionalradio nodes may be added. The evaluation of said areas indicated by thearea filled with L-shaped patterns visualization and the associatedoverall evaluation being low will still be evaluated low even if enoughfingerprints are obtained for said areas.

The area of the venue located on the right of FIG. 5, in which novisualization is overlaid on the map of the venue indicates an area, inwhich no fingerprints are obtained.

The area located directly to the left of the area, in which nofingerprints are obtained, is overlaid by a visualization of a hatchedarea with diagonal lines. This visualization of a hatched area withdiagonal lines indicates that enough radio nodes are comprised by saidarea, but not enough fingerprints are obtained. Thus, furtherfingerprints in said area may be obtained for said area to supportsufficient indoor positioning and/or floor detection performance.

FIG. 6 depicts a sample visualization of a third metric determinedaccording to an exemplary embodiment of a method.

In contrast to the visualization shown in FIG. 5, the visualization ofFIG. 6 comprises explicit suggestions to a user for achieving accurateindoor positioning and/or floor detection. FIG. 6 shows the same map ofthe venue as FIG. 5. For instance, in the upper left corner, there isoverlaid a visualization indicating that additional one or morefingerprints need to be obtained in said area (“Collect more data inthis area!”).

Further, the area of the venue located in the middle of FIG. 6 isoverlaid with a visualization indicating that more radio nodes should beadded to the infrastructure of said area (“Install more beacons in thisarea!”).

In this way, a user may be provided with all information he needs inorder to provide a venue with i) the infrastructure; and for performingindoor positioning and/or floor detection with ii) fingerprints obtainedfor the venue.

The following embodiments shall also be considered to be disclosed:

It is proposed to visualize the overall data quality in a singlecombined view and guide user actions based on the quality view. Theoverall quality may for instance have two components:

-   -   i) Data collection sufficiency, for instance sample density and        coverage; this may for instance follow from the amount labor        invested to the data collection;    -   ii) Infrastructure quality, for instance the quality of the        radio signal environment in the venue (e.g. building); this may        for instance follow directly from the number of radio nodes and        their geometry within the venue (e.g. building).

These two metrics may for instance be evaluated separately and/or becombined, e.g. as described in the table below for easy-to-interpretform for the user:

Infrastructure Quality Low High Data Low Add more radio nodes Collectmore radio data Collection in the affected areas in the affected areasto Sufficiency and collect radio data increase data collection in thoseareas after quality. the installation. High Add more radio nodes Highoverall quality. in the affected areas No actions needed. and collectradio data in those areas after the installation.

The underlying principle is that when the both metrics are high, theoverall quality is high. However, of one or both metrics are low, theuser must take an action—and the action to be taken may for instancedepend on which one of the two metrics is low. Specifically, if theradio infrastructure quality is low, more radio nodes (beacons or Wi-FiAccess Points, to name but a few non-limiting examples) need to beinstalled in the venue (e.g. building) after which data needs to becollected in the area. However, if the radio data collection quality islow, the user can easily correct the situation by collecting more radiodata with the data collection tool.

Importantly, the radio infrastructure quality is considered as a moreprimary metric, because unless radio infrastructure is adequate, noamount of data collection can make the positioning quality high.

Further, it may be noted that when radio data collection is high, butthe infrastructure quality is low, the installation of the radio nodesin the area will initially also decrease the radio data collectionquality, because the radio data for the new radio nodes is not capturedyet. However, as more radio data is collected, the overall quality willeventually be high.

FIG. 5 shows an exemplary radio map as used in a method according to thefirst aspect comprising a quality view of radio data collection.

Several observations may be made:

-   i) The overall quality is high in the upper left corner of the    indoor map: the area has been covered well with the samples and the    radio infrastructure quality is high there;-   ii) There is a small area in the middle and large area at the lower    left corner, which show low radio infrastructure quality. The    infrastructure quality can only be improved by installing more    beacons in the area. After that, further or new radio data    collection is required in that area;-   iii) The rightmost part of the building is still uncovered and thus    nothing can be said about the quality, e.g. of the infrastructure;-   iv) More radio data collection is required in the hatched areas with    diagonal lines. However, radio infrastructure is fine there.

FIG. 6 shows an exemplary visualization of an exemplary user interfaceas used in a method according to the first aspect.

The shown user interface may for instance suggest to the user, what kindof corrective actions the user may need to take. In this way, the toolmay be made user friendly: in the end the user needs to understandneither the data collection requirements nor radio infrastructurerequirements. It suffices to walk around with the tool, which thaninforms the user about the quality and corrective actions.

Further, the following embodiments with respect to the workflow ofexemplary aspects shall also be considered to be disclosed:

Nothing about the quality may be determined before some radio data hasbeen collected from the venue (e.g. building). This is because thesystem cannot know about the quality of the radio infrastructure beforesamples have been obtained of the radio environment. Therefore, withthis type of quality mechanism, the suggested work flow may be asfollows:

-   i) Walk around the venue (e.g. building) along main corridors and    large halls with the tool to get feedback on the radio    infrastructure quality: in case there are areas in which the radio    quality is low, those areas will be indicated with e.g. an area    filled with L-shaped patterns. The areas, in which the radio    infrastructure quality is good, would be shown e.g. with a hatched    area with diagonal lines, because after the single pass along the    corridors and halls the data collection quality is low. The    reasoning for just walking around the building in this way is that    it would be useless to collect radio data densely only to find out    that the radio infrastructure quality is bad.-   ii) The areas showing low radio infrastructure quality (shown in    FIG. 6 with areas filled with L-shaped patterns) need more beacons    (or Wi-Fi Access Points). Beacons need to be added before collecting    more radio data. After installing the additional radio    infrastructure, walk around the area showing low radio quality to    see, if the radio infrastructure is good now. The affected areas    (previously indicated with an area filled with L-shaped patterns)    would turn to an hatched area with diagonal lines because the radio    infrastructure is now good, but the radio data collection quality is    low.-   iii) Collect more radio data in the areas shown as hatched areas    with diagonal lines, the areas will turn to hatched areas with    vertical lines once enough radio data has been collected.

Further, the following embodiments with respect to the metrics ofexemplary aspects shall also be considered to be disclosed:

Technically speaking, the usefulness of quality view depends on how wellthe metrics can really capture the “quality”.

The data collection quality (sufficiency) can be a function of these:

-   i) Sample density:    -   if radio data is collected too sparsely, the full behavior of        the radio signal cannot be captured. Therefore, enough samples        needs to be collected per unit area. However, the quality also        saturates: the quality does not improve after certain threshold        density.-   ii) Similarity of the nearby samples:    -   Another important aspect to consider is the similarity between        the nearby points. If two nearby points show wildly different        set of radio nodes or significantly differing Rx levels, then in        that area there is something (e.g. a wall) that causes the radio        signals change at high rate. In such area radio data needs to be        collected more densely than in other areas to capture the rapid        signal strength changes.

The infrastructure quality can be a function of these:

-   i) Number of radio nodes observed at a point:    -   Simply, more radio nodes there are, the better the        infrastructure quality and positioning accuracy.-   ii) Average or median Rx value:    -   In general, it is advantageous to have high Rx levels, because        there is room for high variability, which helps positioning—the        more variability there is, the more unique the Rx pattern is at        a given point and the higher the positioning accuracy. If the        average signal strength is low, it means that on average there        cannot be much variability in the signal strengths.-   iii) Distribution of the Rx values:    -   This component relates to the previous item. However, here the        distribution of the Rx values may be analyzed and understood. It        does not suffice to have one or two very high Rx values, if the        rest are very low. In such a case the variability is low and        positioning performance will not be too good. Thus, it is        advantageous if the distribution shows that there are multiple        radio nodes with high signal strength.-   iv) Distribution of the Rx gradients:    -   The positioning performance is better, the higher the signal        strength variability is from point-to-point, because in such a        case the nearby points have more unique signal strength        patterns. The points may then for instance be distinguished from        each other more reliably using noise measurements resulting in        higher accuracy.

Further, it may be noted that the radio infrastructure may only beevaluated exactly at the points, where actual data has been collected.However, for the illustrative purposes the quality needs to be indicatedalso for the nearby areas from which data has not been collected. Then,the quality can be estimated with interpolation/extrapolation methods.

Once the two metrics have been evaluated, the final metric can bequalitatively generated as follows in the table below:

Data collection sufficiency Low Medium High Infra- Low Infra-lowInfra-low Infra-low structure Medium Data-low Medium Medium quality HighData-low Medium High

“Infra-low” refers to that the infrastructure quality is low and may becolored, e.g. as a visualization on a radiomap in the color red.

“Data-low” refers to that the data collection sufficiency is low and maybe colored, e.g. as a visualization on a radiomap in the color red.

In order to differentiate between “Infra-low” and “Data-low”, these twocases may for instance be indicated in a visualization in differentways, e.g. “Infra-low” may be colored, e.g. as a visualization on aradiomap in the color red, and “Data-low” may be colored, e.g. as avisualization on a radiomap (e.g. the radiomap used in the visualizationof “Infra-low”) in the color light red, to name but one non-limitingexample.

“Medium” refers to that additional data collection should be performedfor acquiring a sufficient amount of data and may be colored, e.g. as avisualization on a radiomap in the color mild green.

“High” refers to that the infrastructure quality is high and the datacollection sufficiency is high and may be colored, e.g. as avisualization on a radiomap in the color dark green.

In case that both metrics are “high” then the overall metric is “high”.And in case one or the both metrics are “low”, then the overall metricis low. However, in the low end it may for instance be distinguishedwhether the low metric is due to infrastructure or data collectionquality is “low”. This is because the corrective actions are different:

i) Low data collection quality→collect more radio data;ii) Low infrastructure quality→install more beacons and then collectradio data.

Finally, it may not be stressed enough that when giving visual clues,the radio infrastructure quality is more primary than the datacollection one. To exemplify, as indicated in the table above, an areaof the venue shall be shown primarily in e.g. red, or indicated as anarea filled with L-shaped patterns, if needed, because radioinfrastructure need to be secured first and only then the datacollection sufficiency. Thus, in case the quality of the infrastructureis determined to be low, the area of the venue shall be shown primarilyin e.g. red, or indicated as an area filled with L-shaped patterns inthe visualization.

In this way, the user gets timely information on the quality of theradio infrastructure and can react accordingly. Processing powerrequirements for the data analysis may be significant, but due toincreasing processing power capabilities of modern devices more accurateindoor positioning and floor detection can be achieved. Anyhow, sincethe data analysis is regularly performed in a cloud (e.g. server cloud),processing power requirements may not be of further interest.

Further, also the following embodiments shall be considered to bedisclosed:

Embodiment 1

An apparatus comprising at least one processor and at least one memoryincluding computer program code, the at least one memory and thecomputer program code configured to, with the at least one processor,cause the apparatus to at least perform:

-   -   obtaining a plurality of fingerprints, wherein each fingerprint        comprises a piece of position information, and wherein each        fingerprint is gathered in a venue;    -   determining a first metric based at least partially on the        obtained plurality of fingerprints, wherein the first metric is        indicative of a quality value of the obtained plurality of        fingerprints, wherein the first metric indicates for each piece        of position information of the plurality of fingerprints whether        or not the quality and/or a quantity of the obtained        fingerprints with respect to the piece of position information        is sufficient;    -   determining a second metric based at least partially on the        obtained plurality of fingerprints, wherein the second metric is        indicative of a quality value of an infrastructure of the venue,        wherein the plurality of fingerprints are gathered from one or        more radio nodes comprised by the infrastructure, and wherein        the second metric indicates for each of the pieces of position        information of each of the respective fingerprints whether or        not the quality of the infrastructure is sufficient;    -   determining a third metric indicative of an evaluation of the        quality of the infrastructure of the venue based at least        partially on the second metric; and    -   outputting the first metric, the second metric and/or the third        metric.

Embodiment 2

The apparatus according to embodiment 1, wherein the determined thirdmetric is indicative of an evaluation of the quality of the obtainedplurality of fingerprints and the quality of the infrastructure, andwherein the third metric is determined based at least partially on thefirst metric and on the second metric.

Embodiment 3

The apparatus according to any of the preceding embodiments, wherein thequality value of the obtained plurality of fingerprints and/or thequality value of the infrastructure of the venue represent one of thefollowing states i) to iii):

i) infrastructure quality and/or fingerprint quality is low;ii) infrastructure quality and/or fingerprint quality is medium;iii) infrastructure quality and/or fingerprint quality is high.

Embodiment 4

The apparatus according to any of the preceding embodiments, wherein incase the second metric is indicative of the state i) represented by aquality value indicative of the infrastructure quality is low, the thirdmetric is determined to represent an overall low quality state,independent of whether or not the first metric is indicative of that thedata collection quality is sufficient.

Embodiment 5

The apparatus according to any of the preceding embodiments, wherein incase the first metric indicates that the quality of the obtainedplurality of fingerprints with respect to the position information isnot sufficient, at least a part of the outputted first metric representsthat additional one or more fingerprints need to be obtained.

Embodiment 6

The apparatus according to any of the preceding embodiments, wherein incase the second metric indicates that the quality of the infrastructureis not sufficient, at least a part of the outputted second metric and/oroutputted third metric represents that the infrastructure needs to beexpanded, and after the expansion, additional one or more fingerprintsneed to be obtained.

Embodiment 7

The apparatus according to any of the preceding embodiments, wherein thefirst metric is determined based at least partially on a fingerprintdensity analysis, wherein the fingerprint density analysis comprisesanalyzing how many fingerprints of the plurality of fingerprints areassociated with an area of the venue.

Embodiment 8

The apparatus according to any of the preceding embodiments, wherein thefirst metric is determined based at least partially on a similarity ofnearby fingerprints analysis, wherein the similarity of nearbyfingerprints analysis comprises analyzing whether or not at least twofingerprints associated with an area of the venue comprise at leastsimilar identifiers of one or more radio nodes.

Embodiment 9

The apparatus according to any of the preceding embodiments, wherein thesecond metric is determined based at least partially on a number ofradio nodes associated with the position information comprised by eachof the plurality of fingerprints.

Embodiment 10

The apparatus according to any of the preceding embodiments, wherein thesecond metric is determined based at least partially on an average or amedian value of one or more received signal strengths, wherein theaverage or the median value is calculated based on each of the one ormore received signal strengths of the one or more radio nodes of eachrespective fingerprint.

Embodiment 11

The apparatus according to embodiment 9, wherein the second metric isdetermined at least partially based on a distribution analysis of one ormore received signal strengths comprised by each fingerprint, whereineach of the one or more received signal strengths is compared to eachother.

Embodiment 12

The apparatus according to any of the preceding embodiments, wherein thesecond metric is determined at least partially based on a distributionanalysis of gradients of one or more received signal strengths, whereinthe distribution analysis of gradients comprises checking the one ormore received signal strengths of one or more radio nodes associatedwith one or more fingerprints of adjacent locations represented by thepiece of position information of the respective fingerprints forreceived signal strength variability.

Embodiment 13

The apparatus according to any of the preceding embodiments, wherein thethird metric is outputted for usage as a visualization in a radiomap,wherein the visualization represents the evaluation of the obtainedplurality of fingerprints with respect to the position information.

Embodiment 14

The apparatus according to embodiment 13, wherein the visualizationcomprises an indication to one or more locations in the venue whereadditional one or more fingerprints need to be obtained and/or where theinfrastructure need to be expanded.

Embodiment 15

The apparatus according to any of the embodiments 13 or 14, wherein thevisualization is overlayed on a graphical representation of theradiomap.

Embodiment 16

The apparatus according to any of the embodiments 6 to 15, wherein theinfrastructure is expandable by adding one or more radio nodes to theinfrastructure of the venue.

Embodiment 17

A method, in particular performed and/or controlled by at least oneapparatus, the method comprising:

-   -   obtaining a plurality of fingerprints, wherein each fingerprint        comprises a piece of position information, and wherein each        fingerprint is gathered in a venue;    -   determining a first metric based at least partially on the        obtained plurality of fingerprints, wherein the first metric is        indicative of a quality value of the obtained plurality of        fingerprints, wherein the first metric indicates for each piece        of position information of the plurality of fingerprints whether        or not the quality and/or a quantity of the obtained        fingerprints with respect to the piece of position information        is sufficient;    -   determining a second metric based at least partially on the        obtained plurality of fingerprints, wherein the second metric is        indicative of a quality value of an infrastructure of the venue,        wherein the plurality of fingerprints are gathered from one or        more radio nodes comprised by the infrastructure, and wherein        the second metric indicates for each of the pieces of position        information of each of the respective fingerprints whether or        not the quality of the infrastructure is sufficient;    -   determining a third metric indicative of an evaluation of the        quality of the infrastructure of the venue based at least        partially on the second metric; and    -   outputting the first metric, the second metric, and/or the third        metric.

Embodiment 18

The apparatus according to embodiment 17, wherein the determined thirdmetric is indicative of an evaluation of the quality of the obtainedplurality of fingerprints and the quality of the infrastructure, andwherein the third metric is determined based at least partially on thefirst metric and on the second metric.

Embodiment 19

The method according to embodiment 17 or embodiment 18, wherein thequality value of the obtained plurality of fingerprints and/or thequality value of the infrastructure of the venue represent one of thefollowing states i) to iii):

i) infrastructure quality and/or fingerprint quality is low;ii) infrastructure quality and/or fingerprint quality is medium;iii) infrastructure quality and/or fingerprint quality is high.

Embodiment 20

The method according to any of the preceding embodiments, wherein incase the second metric is indicative of the state i) represented by aquality value indicative of the infrastructure quality is low, the thirdmetric is determined to represent an overall low quality state,independent of whether or not the first metric is indicative of that thedata collection quality is sufficient.

Embodiment 21

The method according to any of the preceding embodiments, wherein incase the first metric indicates that the quality of the obtainedplurality of fingerprints with respect to the position information isnot sufficient, at least a part of the outputted first metric representsthat additional one or more fingerprints need to be obtained.

Embodiment 22

The method according to any of the preceding embodiments, wherein incase the second metric indicates that the quality of the infrastructureis not sufficient, at least a part of the outputted second metric and/oroutputted third metric represents that the infrastructure needs to beexpanded, and after the expansion, additional one or more fingerprintsneed to be obtained.

Embodiment 23

The method according to any of the preceding embodiments, wherein thefirst metric is determined based at least partially on a fingerprintdensity analysis, wherein the fingerprint density analysis comprisesanalyzing how many fingerprints of the plurality of fingerprints areassociated with an area of the venue.

Embodiment 24

The method according to any of the preceding embodiments, wherein thefirst metric is determined based at least partially on a similarity ofnearby fingerprints analysis, wherein the similarity of nearbyfingerprints analysis comprises analyzing whether or not at least twofingerprints associated with an area of the venue comprise at leastsimilar identifiers of one or more radio nodes.

Embodiment 25

The method according to any of the preceding embodiments, wherein thesecond metric is determined based at least partially on a number ofradio nodes associated with the position information comprised by eachof the plurality of fingerprints.

Embodiment 26

The method according to any of the preceding embodiments, wherein thesecond metric is determined based at least partially on an average or amedian value of one or more received signal strengths, wherein theaverage or the median value is calculated based on each of the one ormore received signal strengths of the one or more radio nodes of eachrespective fingerprint.

Embodiment 27

The method according to embodiment 26, wherein the second metric isdetermined at least partially based on a distribution analysis of one ormore received signal strengths comprised by each fingerprint, whereineach of the one or more received signal strengths is compared to eachother.

Embodiment 28

The method according to any of the preceding embodiments, wherein thesecond metric is determined at least partially based on a distributionanalysis of gradients of one or more received signal strengths, whereinthe distribution analysis of gradients comprises checking the one ormore received signal strengths of one or more radio nodes associatedwith one or more fingerprints of adjacent locations represented by thepiece of position information of the respective fingerprints forreceived signal strength variability.

Embodiment 29

The method according to any of the preceding embodiments, wherein thethird metric is outputted for usage as a visualization in a radiomap,wherein the visualization represents the evaluation of the obtainedplurality of with respect to the position information.

Embodiment 30

The method according to embodiment 29, wherein the visualizationcomprises an indication to one or more locations in the venue whereadditional one or more fingerprints need to be obtained and/or where theinfrastructure need to be expanded.

Embodiment 31

The method according to any of the embodiments 29 or 30, wherein thevisualization is overlayed on a graphical representation of theradiomap.

Embodiment 32

The method according to any of the embodiments 22 to 31, wherein theinfrastructure is expandable by adding one or more radio nodes to theinfrastructure of the venue.

Embodiment 33

An apparatus configured to perform and/or control or comprisingrespective means for performing and/or controlling the method of any ofthe embodiments 17 to 32.

Embodiment 34

A computer program code, the computer program code when executed by aprocessor causing an apparatus to perform the actions of the methodaccording to any of the embodiments 17 to 32.

Embodiment 35

A computer readable storage medium in which computer program codeaccording to the embodiment 34 is stored.

Embodiment 36

An apparatus comprising at least one processor and at least one memoryincluding computer program code, the at least one memory and thecomputer program code configured to, with the at least one processor,cause an apparatus at least to perform and/or control the method of anyof the embodiments 17 to 32.

Embodiment 37

The apparatus according to embodiment 36, wherein the apparatus is amobile device or a part thereof, and, in particular, a part of anautonomous vehicle.

Embodiment 38

A system, comprising a first apparatus according to anyone ofembodiments 1 to 16, and at least a second apparatus, wherein the secondapparatus is configured to provide the at least one probe data to thefirst apparatus.

Embodiment 39

A system, comprising an apparatus according to any of embodiments 1 to16, and an electronic device, wherein the electronic device isconfigured to gather one or more fingerprints.

Embodiment 40

The system according to embodiment 39, wherein the electronic device isconfigured to provide the one or more fingerprints to the apparatus.

Embodiment 41

A tangible computer-readable medium storing computer program code, thecomputer program code when executed by a processor causing an apparatusto perform and/or control:

-   -   obtaining a plurality of fingerprints, wherein each fingerprint        comprises a piece of position information, and wherein each        fingerprint is gathered in a venue;    -   determining a first metric based at least partially on the        obtained plurality of fingerprints, wherein the first metric is        indicative of a quality value of the obtained plurality of        fingerprints, wherein the first metric indicates for each piece        of position information of the plurality of fingerprints whether        or not the quality and/or a quantity of the obtained        fingerprints with respect to the piece of position information        is sufficient;    -   determining a second metric based at least partially on the        obtained plurality of fingerprints, wherein the second metric is        indicative of a quality value of an infrastructure of the venue,        wherein the plurality of fingerprints are gathered from one or        more radio nodes comprised by the infrastructure, and wherein        the second metric indicates for each of the pieces of position        information of each of the respective fingerprints whether or        not the quality of the infrastructure is sufficient;    -   determining a third metric indicative of an evaluation of the        quality of the infrastructure of the venue based at least        partially on the second metric; and    -   outputting the first metric, the second metric, and/or the third        metric.

In the present specification, any presented connection in the describedembodiments is to be understood in a way that the involved componentsare operationally coupled. Thus, the connections can be direct orindirect with any number or combination of intervening elements, andthere may be merely a functional relationship between the components.

Moreover, any of the methods, processes and actions described orillustrated herein may be implemented using executable instructions in ageneral-purpose or special-purpose processor and stored on acomputer-readable storage medium (e.g., disk, memory, or the like) to beexecuted by such a processor. References to a ‘computer-readable storagemedium’ should be understood to encompass specialized circuits such asFPGAs, ASICs, signal processing devices, and other devices.

The expression “A and/or B” is considered to comprise any one of thefollowing three scenarios: (i) A, (ii) B, (iii) A and B. Furthermore,the article “a” is not to be understood as “one”, i.e. use of theexpression “an element” does not preclude that also further elements arepresent. The term “comprising” is to be understood in an open sense,i.e. in a way that an object that “comprises an element A” may alsocomprise further elements in addition to element A.

It will be understood that all presented embodiments are only exemplary,and that any feature presented for a particular example embodiment maybe used with any aspect of the invention on its own or in combinationwith any feature presented for the same or another particular exampleembodiment and/or in combination with any other feature not mentioned.In particular, the example embodiments presented in this specificationshall also be understood to be disclosed in all possible combinationswith each other, as far as it is technically reasonable and the exampleembodiments are not alternatives with respect to each other. It willfurther be understood that any feature presented for an exampleembodiment in a particular category (method/apparatus/computerprogram/system) may also be used in a corresponding manner in an exampleembodiment of any other category. It should also be understood thatpresence of a feature in the presented example embodiments shall notnecessarily mean that this feature forms an essential feature of theinvention and cannot be omitted or substituted.

The statement of a feature comprises at least one of the subsequentlyenumerated features is not mandatory in the way that the featurecomprises all subsequently enumerated features, or at least one featureof the plurality of the subsequently enumerated features. Also, aselection of the enumerated features in any combination or a selectionof only one of the enumerated features is possible. The specificcombination of all subsequently enumerated features may as well beconsidered. Also, a plurality of only one of the enumerated features maybe possible.

The sequence of all method steps presented above is not mandatory, alsoalternative sequences may be possible. Nevertheless, the specificsequence of method steps exemplarily shown in the figures shall beconsidered as one possible sequence of method steps for the respectiveembodiment described by the respective figure.

The invention has been described above by means of example embodiments.It should be noted that there are alternative ways and variations whichare obvious to a skilled person in the art and can be implementedwithout deviating from the scope of the appended claims.

1. A method comprising: receiving radio data associated with one or moreradio nodes, wherein the received radio data is a candidate to be usedfor indoor position estimation; and outputting, based at least on thereceived radio data, a visualization that includes a textual expressionindicating a suggested action associated with collection of additionalradio data, wherein the additional radio data is also a candidate to beused for indoor position estimation, and wherein the textual expressionindicating the suggested action comprises the textual expressionindicating one or more of (i) an area at which the additional radio datashould be collected or (ii) an area at which additional infrastructureshould be deployed.
 2. The method of claim 1, wherein the indoorposition estimation includes floor detection.
 3. The method of claim 1,wherein the radio data includes position information for at least one ofthe one or more radio nodes and an identifier for at least one of theone or more radio nodes.
 4. The method of claim 1, wherein the radiodata includes a received signal strength for at least one of the one ormore radio nodes.
 5. The method of claim 1, wherein the radio dataincludes density associated with the radio data.
 6. The method of claim1, wherein the radio data includes a quantity associated with the radiodata.
 7. The method of claim 1, further comprising: evaluating the radiodata for performance of an indoor positioning system.
 8. The method ofclaim 7, wherein the evaluation of the radio data includes adistribution analysis of gradients of one or more received signalstrengths for variability.
 9. The method of claim 1, wherein the one ormore radio nodes are in a venue and the suggested action includes anindicator for a location within the venue for the area at which theadditional radio data should be collected or the area at whichadditional infrastructure should be deployed.
 10. The method of claim 9,wherein the visualization includes a map of the venue with the suggestedaction overlaid on the map.
 11. An apparatus comprising at least oneprocessor and at least one memory including computer program code, theat least one memory and the computer program code configured to, withthe at least one processor, cause the apparatus to at least perform:receiving radio data associated with one or more radio nodes, whereinthe received radio data is a candidate to be used for indoor positionestimation; and outputting, based at least on the received radio data, avisualization that includes a textual expression indicating a suggestedaction associated with collection of additional radio data, wherein theadditional radio data is also a candidate to be used for indoor positionestimation, and wherein the textual expression indicating the suggestedaction comprises the textual expression indicating one or more of (i) anarea at which the additional radio data should be collected or (ii) anarea at which additional infrastructure should be deployed.
 12. Theapparatus of claim 11, the at least one memory and the computer programcode configured to, with the at least one processor, cause the apparatusto at least perform: determining a first quality value forinfrastructure of the one or more radio nodes and a second quality forthe radio data of the one or more radio nodes.
 13. The apparatus ofclaim 11, wherein the radio data includes position information for theone or more radio nodes and an identifier for the one or more radionodes.
 14. The apparatus of claim 11, wherein the radio data includesreceived signal strength.
 15. The apparatus of claim 11, wherein theradio data includes density of the radio data.
 16. The apparatus ofclaim 11, wherein the radio data includes a quantity of measurements.17. The apparatus of claim 11, the at least one memory and the computerprogram code configured to, with the at least one processor, cause theapparatus to at least perform: determining a performance of the indoorposition estimate using the radio data.
 18. The apparatus of claim 11,the at least one memory and the computer program code configured to,with the at least one processor, cause the apparatus to at leastperform: determining a distribution analysis of gradients of one or morereceived signal strengths included in the radio data.
 19. The apparatusof claim 11, wherein the one or more radio nodes are in a venue and thesuggested action includes an indicator for one or more locations withthe venue for the area at which the additional radio data should becollected or the area at which additional infrastructure should bedeployed.
 20. A non-transitory computer-readable storage mediumincluding instructions to perform a method comprising: receiving radiodata associated with one or more radio nodes, wherein the received radiodata is a candidate to be used for indoor position estimation; andoutputting, based at least on the received radio data, a visualizationthat includes a textual expression indicating a suggested actionassociated with collection of additional radio data, wherein theadditional radio data is also a candidate to be used for indoor positionestimation, and wherein the textual expression indicating the suggestedaction comprises the textual expression indicating one or more of (i) anarea at which the additional radio data should be collected or (ii) anarea at which additional infrastructure should be deployed.